Authors:
Jürgen Bernard
1
;
Christian Ritter
1
;
David Sessler
1
;
Matthias Zeppelzauer
2
;
Jörn Kohlhammer
3
and
Dieter Fellner
3
Affiliations:
1
Technische Universität Darmstadt, Germany
;
2
St. Pölten University of Applied Sciences, Austria
;
3
Technische Universität Darmstadt and Fraunhofer Institute for Computer Graphics Research, Germany
Keyword(s):
Information Visualization, Visual Analytics, Active Learning, Similarity Search, Similarity Learning, Distance Measures, Feature Selection, Complex Data Objects, Soccer Player Analysis, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Information and Scientific Visualization
;
Interface and Interaction Techniques for Visualization
;
Visual Data Analysis and Knowledge Discovery
;
Visual Representation and Interaction
;
Visualization Applications
Abstract:
The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information
retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity
of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking
the example of soccer players, we present a visual-interactive system that learns users’ mental models of
similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their
subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of
similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the
model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach
is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.